Skip to main content

The AURORA Financial Management System: Model and Parallel Implementation Design

Abstract

The AURORA financial management system under development at the University of Vienna is a modular decision support tool for portfolio and asset–liability management. It is based on a multivariate Markovian birth-and-death factor model for the economic environment, a pricing model for the financial instruments and an objective function which is flexible enough to express risk aversion.

The core of the system is a large scale linear or convex program, which due to its size and structure is well suited for parallel optimization methods.

As the system is still at an early stage of development, the results are preliminary in nature. Only a few types of financial instruments are handled and just two types of objectives are considered. The parallel optimization modules are still in the development phase.

This is a preview of subscription content, access via your institution.

References

  1. Ph. Artzner, F. Delbaen, J.-M. Eber and D. Heath, Coherent measures of risk, Mathematical Finance 9 (1999) 203–228.

    Google Scholar 

  2. D.P. Bertsekas, Constrained Optimization and Lagrange Multiplier Methods (Academic Press, 1982).

  3. J.R. Birge, Decomposition and partitioning methods for multistage stochastic linear programs, Operations Research 33 (1985) 989–1007.

    Google Scholar 

  4. J.R. Birge and L. Qi, Computing block-angular Karmarkar projections with applications to stochastic programming, Management Science 34(12) (1990).

  5. R.E. Bixby, Progress in linear programming, ORSA J. on Computing 6(1) (1994) 15–22.

    Google Scholar 

  6. F. Black, E. Derman and W. Toy, A one-factor model of interest rates and its application to tresury bond options, Financial Analysts Journal 3 (January/February 1990) 33–39.

    Google Scholar 

  7. M.J. Brennan, E.S. Schwartz and R. Lagnado, Strategic asset allocation, Journal of Economic Dynamics and Control 21 (1997) 1377–1403.

    Google Scholar 

  8. D.R. Cariño, T. Kent, D.H.Myers, C. Stacy, M. Sylvanus, A.L. Turner, K.Watanabe and W.T. Ziemba, The Russell-Yasuda Kasai model: an asset/liability model for a Japanese insurance company using multistage stochastic programming, Interfaces 24 (1994) 29–49.

    Google Scholar 

  9. J.C. Cox, J.E. Ingersoll and S.A. Ross, A theory of terms of interest rates, Econometrica 53 (1985) 385–407.

    Google Scholar 

  10. G.B. Dantzig and P. Wolfe, Decomposition principle for linear programs, Operations Research 8 (1960) 101–111.

    Google Scholar 

  11. M.A.H. Dempster and R.T. Thompson, Parallelization and aggregation of nested Benders decomposition, Working paper WP 01/95, The Judge Institute of Management Studies, Cambridge University (1995).

  12. J. Dupacova, Stochastic programming models in banking, Working paper, International Institute for Applied Systems Analysis (1991).

  13. A. Gupta, G. Karypis and V. Kumar, Highly scalable parallel algorithms for sparse matrix factorization, IEEE Transactions on Parallel and Distributed Systems 8(5) (1997).

  14. P. Hoel, S. Port and Ch. Stone, Introduction to Stochastic Processes (Houghton-Mifflin, Boston, 1972).

    Google Scholar 

  15. P. Kall and S.W.Wallace, Stochastic Programming (Wiley, Chichester, 1994).

    Google Scholar 

  16. S. Karlin and H. Taylor, A Second Course in Stochastic Processes (Academic Press, Boston, 1993).

    Google Scholar 

  17. H. Konno and H. Yamazaki, Mean absolute deviation portfolio optimization model and its applications to Tokyo stock market, Management Science 37 (1991) 519–531.

    Google Scholar 

  18. M.I. Kusy and W.T. Ziemba, A bank asset and liability management model, Operations Research 34 (1986) 356–376.

    Google Scholar 

  19. H. Markowitz, Portfolio selection, Journal of Finance 7 (1952) 77–91.

    Google Scholar 

  20. J.M. Mulvey, Nonlinear network models in finance, in: Advances in Mathematical Programming and Financial Planning (JAI Press, 1987).

  21. J.M. Mulvey, An asset-liability investment system, Interfaces 24 (1994) 22–33.

    Google Scholar 

  22. J.M.Mulvey, Multi-stage financial planning systems, in: Operations Research Models in Quantitative Finance, eds. R.L. D'Ecclesia and S.A. Zenios (Physica-Verlag, 1994) pp. 18–35.

  23. J.M. Mulvey and A. Ruszczy´nski, A diagonal quadratic approximation method for large-scale linear programs, Operations Research Letters 12 (1992) 205–215.

    Google Scholar 

  24. J.M. Mulvey and A. Ruszczy´nski, A new scenario decomposition method for large-scale stochastic optimization, Operations Research 43 (1995) 477–490.

    Google Scholar 

  25. J.M. Mulvey and W.T. Ziemba, Worldwide Asset and LiabilityModeling (Cambridge University Press, 1998).

  26. J.M. Mulvey and H. Vladimirou, Stochastic network programming for financial planning problems, Management Science 38 (1992) 1642–1664.

    Google Scholar 

  27. V. Norkin, G.Ch. Pflug and A. Ruszczy´nski, A branch and bound method for stochastic global optimization, Mathematical Programming 83 (1998) 425–450.

    Google Scholar 

  28. W. Ogryczak and A. Ruszczy´nski, From stochastic dominance to mean-risk models: Semideviations as risk measures, European Journal of Operations Research 116 (1999) 33–50.

    Google Scholar 

  29. G.Ch. Pflug, Risk-reshaping contracts and stochastic optimization, Central European Journal of Operations Research 5(3–4) (1998) 205–230.

    Google Scholar 

  30. G.Ch. Pflug, How to Measure Risk? Festschrift to F. Ferschl (Physica-Verlag, 1999).

  31. G.Ch. Pflug and A. ´Swi¸etanowski, Dynamic asset allocation under uncertainty for pension fund management, Control and Cybernetics 28(4) (1999).

  32. A. Ruszczy´nski and A. ´Swi¸etanowski, Accelerating the regularized decomposition method for two stage stochastic linear problems, European Journal of Operations Research 101(2) (1997) 328–342.

    Google Scholar 

  33. A. Ruszczy´nski, An augmented Lagrangian decomposition method for block diagonal linear programming problems, Operations Research Letters 8 (1989) 287–294.

    Google Scholar 

  34. A. Ruszczy´nski, Interior point methods in stochastic programming, Technical Report WP–93–8, International Institute for Applied Systems Analysis, Laxenburg, Austria (1993).

  35. A. Ruszczy´nski, Parallel decomposition of multistage stochastic programming problems, Mathematical Programming 58 (1993) 201–228.

    Google Scholar 

  36. A. Ruszczy´nski, On convergence of an augmented Lagrangian decomposition method for sparse convex optimization, Mathematics of Operations Research 20(3) (1995) 634–656.

    Google Scholar 

  37. O.A. Vasicek, An equilibrium characterization of the term structure, J. Financial Economics 5 (1977) 177–18.

    Google Scholar 

  38. H. Vladimirou and S.A. Zenios, Scalable parallel computations for large-scale stochastic programming, Annals of Operations Research 90 (1999) 87–129.

    Google Scholar 

  39. S.A. Zenios, Financial Optimization (Cambridge University Press, 1993).

  40. S.A. Zenios and R.L. D'Ecclesia, eds., Operations Research Models in Quantitative Finance (Springer, 1994).

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pflug, G., Świętanowski, A., Dockner, E. et al. The AURORA Financial Management System: Model and Parallel Implementation Design. Annals of Operations Research 99, 189–206 (2000). https://doi.org/10.1023/A:1019297118383

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1019297118383